I am sceptical if these persona based agents really make that much of a difference, and more "appear" to make a difference because of their talk style. Underneath is just a system prompt, or more likely a prompt layered on top "You are a frontend engineer, competent in react and Next.js, tailwind-css" - the stack details and project layout, key information is already in the CLAUDE.md. For more stuff the model is goin…
So far, I find it much more important to define task scope and boundaries. If I want to implement a non-trivial feature, I'll have one role for analyzing the problem and coming up with a high-level plan, and then another role for breaking that plan down into very small atomic steps. I'll then pass each step to an implementation role and give it both the high-level plan and the whole list of individual steps as context, while making it clear that the scope is only to implement that one specific step.
I've had very good results with this so far, and once the two main documents are done, I can automate this with a small orchestration script (that does not depend on an LLM and is completely deterministic) going through the list and passing each item to an implementation agent sequentially, even letting the agent create a commit message after every step so I can trace its work afterwards. I've had very clean long-running tasks this way with minimal need for fixing things afterwards. I can go to bed in the evening and launch it and wake up to a long list of commits.
With the new 6 dollar subscription by Z.ai which includes 120 prompts (around 2000 requests) every 5 hours, I can pretty much let this run without having to worry about exceeding my limits.